基于采样汇集网络的场景深度估计

针对现有场景深度估计方法中,由于下采样操作引起的复杂物体边界定位不准确,而造成物体边界处的场景深度估计模糊的问题,受密集网络中特征汇集过程的启发,本文提出一种针对上;下采样过程的汇集网络模型.在下采样过程中,使用尺度特征汇集策略,兼顾不同尺寸物体的估计;在上采样过程中,使用上采样反卷积恢复图像分辨率;同时,引入采样跨层汇集策略,提供下采样过程中保存的物体边界的有效定位信息.本文提出的采样汇集网络(Sampling aggregate network,SAN)中使用的尺度特征汇集和采样跨层汇集,都可以有效缩短特征图到输出损失之间的路径,从而有利于避免模型的参数优化时陷入局部最优解.在公认场景深度估计NYU-Depth-v2数据集上的实验说明,本文方法能够有效改善复杂物体边界等干扰情况下的场景深度估计效果,并在深度估计误差和准确性上,优于当前场景深度估计的主流方法.

Sampling Aggregate Network for Scene Depth Estimation

State-of-the-art approaches for scene depth estimation are built on downsampling strategy, which can lead to inaccurate location and ambiguous depth estimation for complicated boundary. Inspired with feature aggregation in DenseNets, we propose a novel feature aggregation strategy for upsample/downsample in our sampling aggregate network (SAN). Firstly, scale feature aggregation is used in downsample process to consider various scale object boundaries. Secondly, transposed convolution is applied in upsample process to restore image resolution. Thirdly, sample skip connection and aggregation is devoted to extract effective location of object boundary from downsample module with the same resolution. We adopt scale feature aggregation and sample skip aggregation to shorten the path from feature map to output loss, in order to avoid local optimal solution of our sampling aggregate network. Experiments in the recognized NYU-Depth-v2 database of scene depth estimation show that our model can improve the depth estimation result under complecated object boundaries and other disturbances. Our sampling aggregate network outperforms the state-of-the-art methods in error and accuracy evaluations.